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Ying Chen · James Martin

32 citations0/0 files storedInternational Workshop on Semantic Evaluation (SemEval)

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Data Quality and Management Natural Language Processing Techniques Topic Modeling

Abstract

The increasing number of web sources is exacerbating the named-entity ambiguity problem. This paper explores the use of various token-based and phrase-based features in unsupervised clustering of web pages containing personal names. From these experiments, we find that the use of rich features can significantly improve the disambiguation performance for web personal names.

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References

2003A comparison of string distance metrics for name-matching tasks 1999The State of Record Linkage and Current Research Problems 2003Unsupervised personal name disambiguation 2005Name Discrimination by Clustering Similar Contexts 2005Unsupervised Name Disambiguation via Social Network Similarity 2007Towards Robust Unsupervised Personal Name Disambiguation

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